Adaptive Retrieval Scoring for Context-Aware Search Ranking
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Solution Overview
Problem
Conventional information retrieval systems struggle to dynamically adapt to the context of queries, leading to reduced performance and effectiveness in obtaining relevant search results, especially when dealing with different types of data such as tabular data or structured documents, and when integrated with large language models, resulting in incomplete or ambiguous responses.
Innovation Solution
An adaptive information retrieval system that combines semantic and lexical scoring, dynamically adjusting the influence of semantic and lexical relevance based on query context, using a semantic vector space for conceptual understanding and an inverted index for keyword matching, and integrating scores to provide lexically precise and conceptually relevant results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic search is used to understand user intent and context, then search relevance is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent merges semantic search and lexical search into a unified retrieval system. The semantic search component captures user intent and context, while the lexical search component provides fast keyword matching. By combining both approaches, the system achieves high search relevance without the full processing time cost of semantic search alone, as lexical search quickly filters candidates before semantic scoring is applied to a smaller subset.
Solution Approach 2:
The retrieval process is segmented into multiple stages: first lexical search for quick keyword matching to generate candidate documents, then semantic search to score and rank these candidates based on user intent and context. This segmentation allows the system to use fast lexical matching for initial filtering while reserving more computationally intensive semantic analysis for a smaller set of promising documents, thereby reducing overall processing time while maintaining high relevance.
2Measurement precision
If complex algorithms and machine learning models are used for semantic search, then search accuracy is improved, but system complexity and maintenance requirements increase
Solution Approach 1:
The system combines simple lexical search algorithms with semantic search algorithms in a hybrid architecture. The lexical search component uses straightforward keyword matching and indexing structures, while the semantic search component employs machine learning models for intent understanding. This merging allows the system to achieve high search accuracy through semantic understanding while keeping the overall system manageable by using simple lexical methods for initial retrieval and only applying complex semantic analysis where needed.
Solution Approach 2:
The patent introduces an intermediary scoring mechanism that bridges lexical and semantic search results. Instead of directly using complex machine learning models for all retrieval operations, the system uses lexical matching as an intermediary step to pre-filter documents, then applies semantic scoring only to the remaining candidates. This intermediary approach reduces the direct impact of complex algorithms on system complexity while maintaining high accuracy through semantic understanding of the filtered results.
3Productivity
If lexical search is used for keyword matching, then processing speed is improved, but search relevance deteriorates due to lack of context understanding
Solution Approach 1:
The retrieval process is divided into two segments: a fast lexical search segment that handles keyword matching and initial document filtering, and a more thorough semantic search segment that analyzes user intent and context. The lexical search segment operates at high speed with simple algorithms, while the semantic search segment provides deeper relevance analysis. By segmenting the process, the system achieves both processing speed from lexical search and search relevance from semantic search, with each segment handling the tasks it is optimized for.
Solution Approach 2:
The system applies different quality levels of search at different stages: lexical search provides fast, basic keyword matching quality for initial retrieval, while semantic search provides high-quality contextual understanding for final ranking. This local quality approach allows the system to use simple, fast algorithms where speed is critical (initial filtering) and reserves complex, high-accuracy algorithms for where relevance is most important (final ranking), thereby achieving both processing speed and search relevance.
Data Source
AI summary
Certain aspects of the disclosure provide a method of adaptive information retrieval based on both lexical and semantic relevance. In some aspects, the method includes identifying a plurality of documents based on a context of the query request, assigning an integrated score for each respective document of the plurality of documents based on a semantic score for the respective document and a lexical score for the respective document, and ranking each document of the plurality of documents based on the integrated score.